1. Field of the Invention
The present invention relates to a technique of classification of an image of an object to be inspected.
2. Description of the Background Art
In a process of manufacturing a semiconductor substrate, a glass substrate, a printed circuit board, a mask used for exposure of a substrate or the like (all of which will be inclusively referred to as a “substrate”), visual inspection has been performed using an optical microscope or a scanning electron microscope, for example, to detect the existence of defects such as foreign objects, flaws, or etching failures. These defects thus detected in the inspection process have conventionally underwent detailed analysis. As a result, the cause of the defects have been specified, to take action such as countermeasures in response to the defects.
A substrate has been patterned with complicated and fine pattern features recently, so that the type and the amount of the detected defects are increasing. In response, auto defect classification (hereinafter referred to as “ADC”) has been suggested which allows automatic classification of each defect detected in the inspection process into a class to be included (hereinafter referred to as a “category”). Even when various types of defects are detected in large quantity, ADC allows rapid analysis of the defects with high degree of efficiency. By way of example, attention may be directed to a category including defects with a high frequency of occurrence among those classified by ADC, so that such category can be given high priority for its countermeasures.
Automatic classification of the results of inspection is not limited to ADC for classifying defects, but it is also directed to various objects. For example, as a classifier for making classification into different categories, a neural network, a decision tree, discriminant analysis and the like, are employed.
In order for the classifier to be operative to perform automatic classification, training data responsive to a desired category is prepared in advance, and learning is necessary to the classifier. By way of example, in the classification under ADC, an operator observes a plurality of defect images, determines a category suitable for each defect image and teaches the result of determination, whereby the training data is created.
The performance of automatic classification largely depends on the quality of the training data to be learned by the classifier. In order to provide high quality of the training data, the operator is required a large amount of teaching work with high precision, taking a good deal of effort. In view of this, an environment for efficiently assisting the operator has been required to realize teaching work with sufficient rapidity and high precision.
When correction of or addition to existing data is to be taught, the operator should be provided with information to determine whether modification necessitated by the correction or addition is reasonable. Otherwise, such modification may not necessarily result in improvement in quality of the training data.
In order to assist classification by the operator, images may be arranged and displayed on the basis of feature values of the images, the exemplary technique of which is disclosed in Japanese Patent Application Laid-Open No. 2001-156135. However, information other than feature values is not used for assisting classification, and therefore, the operator cannot be provided with information for assisting the operator to determine whether decline in quality of the training data occurs due to the conditions for calculating feature values (in other words, the image is singular) or not. Due to this, this technique cannot necessarily provide the environment for adequately and efficiently performing classification.
In a so-called in-line inspection system including connection of an inspection apparatus and a classification apparatus for performing ADC, an image obtained by the inspection apparatus has a low resolution. Therefore, inadequate teaching work by the operator will quite likely. Further, although the inspection apparatus creates various types of useful information for classification and the classification apparatus creates useful information for inspection, effective use of such information has not been allowed.